IT-TextFusion: Iterative Text-Image Interaction with Text-Guided Residual Refinement for Degradation-Aware Image Fusion

2026-09-01

AI Summary

为解决现有文本引导图像融合方法处理复杂退化能力有限的问题，提出了一种迭代文本-图像交互框架，通过多层次特征融合和残差细化增强鲁棒性。

Abstract

Text-guided image fusion has recently emerged as an effective paradigm for integrating multi-modal information while enabling flexible and task-oriented fusion control. However, existing text-guided fusion methods often rely on shallow semantic-visual interaction and limited attention mechanisms, which restrict their ability to robustly handle complex degradations and fully exploit textual guidance. In this paper, we propose an iterative text-guided image fusion framework that incorporates text-conditioned feature interaction across multiple fusion and refinement stages. The proposed method integrates deepest-level Cross-Attention, multi-scale Cross-Gate Fusion, and stage-specific text-conditioned modulation, allowing the global text embedding to condition hierarchical feature fusion and residual refinement. By repeatedly injecting the pooled text embedding across hierarchical decoder and refinement stages, the proposed framework provides degradation-aware global semantic conditioning while preserving complementary information from the visible and infrared modalities. Experiments on several benchmark datasets show that the proposed method improves several information-preservation and perceptual-quality metrics, while exhibiting metric-dependent trade-offs on some datasets.

Problem

• text-guided image fusion
• semantic-visual interaction
• attention mechanisms
• degradation handling
• textual guidance

Innovation

• Iterative Text-Image Interaction
• Text-Guided Residual Refinement
• Cross-Attention
• Multi-scale Cross-Gate Fusion
• Degradation-Aware